Machine Learning Exercise

Question 1:

The dataset contains the collection that appeared on the Reuters newswire in 1987. The documents were assembled and indexed with categories by personnel from Reuters Ltd.

Formatting of the documents and production of associated data files was done in 1990 by Devid D. Lewis and Stephen Harding at the Information Retrieval Laboratory. The articles are related to category Money and crude which are target classes

Problem statements:

• Load the dataset from the location of the file provided in “input Format” using pandas

1) DESCRIPTION

80% of people who purchase car insurance are men. If the owners of 9 car insurance are randomly selected, then find the probability using binomial distribution that exactly X out of them are men

• Read a number X from a line of input

• Print the output rounded till 4 decimal point

Example:

Sample Input:

Here is list of complex topics for Projects and Assignment Help

• Key concepts Supervised/Unsupervised Learning

• Probability Theory, Probabilistic graphical models ,HMM ,MRF

• Bayesian Networks ,Inference ,Loss functions and generalization

• Linear Modeling, Nonlinear Dimension Reduction, Maximum Entropy

• Exponential Family Models, Conditional Random Fields, Graphical Models

In Machine learning, Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. Variations of the tf-IDF weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query.

How to calculate tf-idf:

There is a simple way to calculate tf=idf in machine learning-

First, calculate tf (term frequency)

TF: Term Frequency, which measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much…